Xianghui Yuan

Adaptive turn rate estimation using range rate measurements

The coordinated turn (CT) model is often used to track maneuvering target which performs CT motion. The key point to the successful use of this model is to determine the turn rate parameter. A new method to estimate the turn rate by using radar range-rate measurements is presented. First, four possible turn rates can be achieved with the range-rate measurements. Second, the minimum turn rate and its opposite value are chosen to be the possible turn rates. Finally, an interacting multiple model (IMM) algorithm with one constant velocity (CV) model and two CT models is designed to track the maneuvering target which performs uniform and ct motions. Monte-Carlo simulation results show that this algorithm is not only better than equivalent noise approach in tracking performance, but also better than the conventional IMM algorithm with the adaptive turn rate model. Further simulations show that the algorithm is robust with the accuracy of the range-rate measurements

Models and Algorithms for Tracking Target with Coordinated Turn Motion

Tracking target with coordinated turn (CT) motion is highly dependent on the models and algorithms. First, the widely used models are compared in this paper—coordinated turn (CT) model with known turn rate, augmented coordinated turn (ACT) model with Cartesian velocity, ACT model with polar velocity, CT model using a kinematic constraint, and maneuver centered circular motion model. Then, in the single model tracking framework, the tracking algorithms for the last four models are compared and the suggestions on the choice of models for different practical target tracking problems are given. Finally, in the multiple models (MM) framework, the algorithm based on expectation maximization (EM) algorithm is derived, including both the batch form and the recursive form. Compared with the widely used interacting multiple model (IMM) algorithm, the EM algorithm shows its effectiveness.

Multiple-Model Cardinality Balanced Multitarget Multi-Bernoulli Filter for Tracking Maneuvering Targets

By integrating the cardinality balanced multitarget multi-Bernoulli (CBMeMBer) filter with the interacting multiple models (IMM) algorithm, an MM-CBMeMBer filter is proposed in this paper for tracking multiple maneuvering targets in clutter. The sequential Monte Carlo (SMC) method is used to implement the filter for generic multi-target models and the Gaussian mixture (GM) method is used to implement the filter for linear-Gaussian multi-target models. Then, the extended Kalman (EK) and unscented Kalman filtering approximations for the GM-MM-CBMeMBer filter to accommodate mildly nonlinear models are described briefly. Simulation results are presented to show the effectiveness of the proposed filter.

Parameter estimation of K distribution based on second-kind statistics

The parameters of K distribution are estimated in this paper, and the log-cumulant estimator is proposed based on second-kind statistics. The performance of the log-cumulant estimator is tested on the Monte Carlo simulations. Parameter estimation results demonstrate that the log-cumulant estimator leads to high estimation accuracy for the small values of the shape parameter.

Sensor selection based on maximum entropy fuzzy clustering for target tracking in large-scale sensor networks

This study proposes a sensor selection approach based on maximum entropy fuzzy clustering to address the target tracking problem in large-scale sensor networks. The authors try to deal with this problem at two levels: (i) sensor-level tracking: data association problem and sensor-level tracking are carried out at the local level, and only the track outputs are transmitted to the fusion centre for data fusion; (ii) global-level fusion: two sensor selection strategies are adopted at the fusion centre, which seek to only choose a subset of reliable sensors for track-to-track fusion and bias registration. In addition, an improved sensor selection approach is proposed for data fusion in both sparse and dense target environments, and a new fuzzy membership reconstruction strategy is introduced for data association in dense target environments. Furthermore, the proposed sensor selection strategy is also effective in the presence of the possible changing sensor biases. Simulation results are given to evaluate the performance of the proposed approaches.

Fusion performance analysis with the correlation

Estimation fusion performance with the effect of correlation is analyzed. First, the correlation type is classified into two categories: same source correlation and different source correlation. Second, with the best linear unbiased estimation (BLUE) criteria, the scalar case is analyzed with the effect of correlation. For the same source correlation, with the same absolute value, the fusion result for negative correlation is better than positive correlation. For the different source correlation, with independent extra information, stronger correlation can lead to better fusion result. Finally, the conclusions are supported by some examples.

A Maneuver Detection Method using Multiple Doppler Radars

Maneuver detection is an important issue in many adaptive tracking algorithms. In radar tracking system, for the lack of target acceleration measurement, maneuver detection is a complicated problem. A new maneuver detection method using multiple Doppler radars is presented. When radars are at different location, their Doppler measurements can be used to estimate the targets tangential and centripetal acceleration, which are the indicator of the maneuver. Compared with the widely used chi-square maneuver detection method, simulation results show that the new method can detect the maneuver more rapidly and has better tracking performance.

Error bounds for joint detection and estimation of multiple unresolved target-groups

An error bound for JDE of multiple unresolved target-groups is derived by RFS.The bound is based on OSPA distance rather than Euclidean distance.The bound is discussed for the special cases when the group number is known or is at most one.Three examples are presented to verify the effectiveness of the bound. According to random finite set (RFS) and information inequality, this paper derives an error bound for joint detection and estimation (JDE) of multiple unresolved target-groups in the presence of clutters and missed detections. The JDE here refers to determining the number of unresolved target-groups and estimating their states. In order to obtain the results of this paper, the states of the unresolved target-groups are modeled as a multi-Bernoulli RFS first. The point cluster model proposed by Mahler is used to describe the observation likelihood of each group. Then, the error metric between the true and estimated state sets of the groups is defined by the optimal sub-pattern assignment distance rather than the usual Euclidean distance. The maximum a posteriori detection and unbiased estimation criteria are used in deriving the bound. Finally, we discuss some special cases of the bound when the number of unresolved target-groups is known a priori or is at most one. Example 1 shows the variation of the bound with respect to the probability of detection and clutter density. Example 2 verifies the effectiveness of the bound by indicating the performance limitations of the cardinalized probability hypothesis density and cardinality balanced multi-target multi-Bernoulli filters for unresolved target-groups. Example 3 compares the bound of this paper with the (single-sensor) bound of 4 for the case of JDE of a single unresolved target-group. At present, this paper only addresses the static JDE problem of multiple unresolved target-groups. Our future work will study the recursive extension of the bound proposed in this paper to the filtering problems by considering the group state evolutions.

Performance Analysis for Distributed Fusion with Different Dimensional Data

Different sensors or estimators may have different capability to provide data. Some sensors can provide a relatively higher dimensional data, while other sensors can only provide part of them. Some estimators can estimate full dimensional quantity of interest, while others may only estimate part of it due to some constraints. How is such kind of data with different dimensions fused? How do the common part and the uncommon part affect each other during fusion? To answer these questions, a fusion algorithm based on linear minimum mean-square error (LMMSE) estimation is provided in this paper. Then the fusion performance is analyzed, which is the main contribution of this work. The conclusions are as follows. First, the fused common part is not affected by the uncommon part. Second, the fused uncommon part will benefit from the common part through the cross-correlation. Finally, under certain conditions, both the more accurate common part and the stronger correlation can result in more accurate fused uncommon part. The conclusions are all supported by some tracking application examples.